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Single Layer Recurrent Neural Network for detection of swarm-like earthquakes in W-Bohemia/Vogtland - the method
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SYSNO ASEP 0462100 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title Single Layer Recurrent Neural Network for detection of swarm-like earthquakes in W-Bohemia/Vogtland - the method Author(s) Doubravová, Jana (GFU-E) ORCID, RID
Wiszniowski, J. (PL)
Horálek, Josef (GFU-E) ORCID, RIDSource Title Computers and Geosciences. - : Elsevier - ISSN 0098-3004
Roč. 93, August (2016), s. 138-149Number of pages 12 s. Publication form Print - P Language eng - English Country GB - United Kingdom Keywords event detection ; artificial neural network ; West Bohemia/Vogtland Subject RIV DC - Siesmology, Volcanology, Earth Structure R&D Projects GAP210/12/2336 GA ČR - Czech Science Foundation (CSF) LM2010008 GA MŠMT - Ministry of Education, Youth and Sports (MEYS) Institutional support GFU-E - RVO:67985530 UT WOS 000379561600015 EID SCOPUS 84971672812 DOI 10.1016/j.cageo.2016.05.011 Annotation In this paper, we present a new method of local event detection of swarm-like earthquakes based on neural networks. The proposed algorithm uses unique neural network architecture. It combines features used in other neural network concepts such as the Real Time Recurrent Network and Nonlinear Auto regressive Neural Network to achieve good performance of detection. We use the recurrence combined with various delays applied to recurrent inputs so the network remembers history of many samples. This method has been tested on data from a local seismic network in West Bohemia with promising results. We found that phases not picked in training data diminish the detection capability of the neural network and proper preparation of training data is therefore fundamental. To train the network we define a parameter called the learning importance weight of events and show that it affects the number of acceptable solutions achieved by many trials of the Back Propagation Through Time algorithm. We also compare the individual training of stations with training all of them simultaneously, and we conclude that results of joint training are better for some stations than training only one station. Workplace Geophysical Institute Contact Hana Krejzlíková, kniha@ig.cas.cz, Tel.: 267 103 028 Year of Publishing 2017
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